At present, autonomous driving decision-making solutions take few elements into account
while ignoring the unpredictable nature of driving behavior, which makes it challenging to …

Safety is one of the major concerns in autonomous driving tasks, and enhancing the
collision avoidance ability of connected and autonomous vehicles (CAVs) is an effective way …

This paper proposed a deep reinforcement learning based reference speed planning
strategy to co-optimize the fuel economy, driving safety, and travel efficiency of connected …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

Decision making is a fundamental component to ensure safe autonomous driving in
highway scenarios. The mainstream architecture for this task is the classical deep Q learning …

Intelligent decisions for autonomous lane-changing in vehicles have consistently been a
focal point of research in the industry. Traditional lane-changing algorithms, which rely on …

Decision-making strategy for autonomous vehicles de-scribes a sequence of driving
maneuvers to achieve a certain navigational mission. This paper utilizes the deep …

Uncertain emergency events are inevitable and occur unpredictably on the highway.
Emergencies with lane capacity drops cause local congestion and can even cause a second …

Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …

This paper investigates how a Bayesian reinforcement learning method can be used to
create a tactical decision-making agent for autonomous driving in an intersection scenario …